Overview

Dataset statistics

Number of variables17
Number of observations160112
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.8 MiB
Average record size in memory136.0 B

Variable types

Numeric9
Categorical8

Alerts

RTPR has constant value "0" Constant
CMD has a high cardinality: 406 distinct values High cardinality
ts is highly correlated with Status and 2 other fieldsHigh correlation
PID is highly correlated with typeHigh correlation
TRUN is highly correlated with State and 1 other fieldsHigh correlation
TSLPI is highly correlated with PRIHigh correlation
NICE is highly correlated with PRIHigh correlation
PRI is highly correlated with POLI and 3 other fieldsHigh correlation
CPUNR is highly correlated with RTPRHigh correlation
EXC is highly correlated with RTPRHigh correlation
CPU is highly correlated with TRUNHigh correlation
TSLPU is highly correlated with StateHigh correlation
POLI is highly correlated with PRI and 2 other fieldsHigh correlation
RTPR is highly correlated with State and 5 other fieldsHigh correlation
Status is highly correlated with ts and 5 other fieldsHigh correlation
State is highly correlated with TRUN and 4 other fieldsHigh correlation
label is highly correlated with ts and 2 other fieldsHigh correlation
type is highly correlated with ts and 3 other fieldsHigh correlation
EXC is highly skewed (γ1 = 101.0338824) Skewed
TRUN has 147066 (91.9%) zeros Zeros
TSLPI has 13983 (8.7%) zeros Zeros
NICE has 117993 (73.7%) zeros Zeros
PRI has 4315 (2.7%) zeros Zeros
CPUNR has 42811 (26.7%) zeros Zeros
EXC has 159514 (99.6%) zeros Zeros
CPU has 116771 (72.9%) zeros Zeros

Reproduction

Analysis started2022-11-12 02:32:13.118327
Analysis finished2022-11-12 02:33:17.648247
Duration1 minute and 4.53 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

ts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct141852
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1555145386
Minimum1554218915
Maximum1556549129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:18.095706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1554218915
5-th percentile1554258943
Q11554419054
median1554619192
Q31556222938
95-th percentile1556365714
Maximum1556549129
Range2330214
Interquartile range (IQR)1803884.25

Descriptive statistics

Standard deviation881681.2642
Coefficient of variation (CV)0.0005669445905
Kurtosis-1.686601961
Mean1555145386
Median Absolute Deviation (MAD)300560
Skewness0.4755534352
Sum2.489974381 × 1014
Variance7.773618516 × 1011
MonotonicityNot monotonic
2022-11-12T08:03:18.745450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15562081933
 
< 0.1%
15562266733
 
< 0.1%
15562267483
 
< 0.1%
15562267433
 
< 0.1%
15562267383
 
< 0.1%
15562267333
 
< 0.1%
15562267283
 
< 0.1%
15562267183
 
< 0.1%
15562267133
 
< 0.1%
15562267083
 
< 0.1%
Other values (141842)160082
> 99.9%
ValueCountFrequency (%)
15542189151
< 0.1%
15542189201
< 0.1%
15542189251
< 0.1%
15542189301
< 0.1%
15542189351
< 0.1%
15542189401
< 0.1%
15542189451
< 0.1%
15542189501
< 0.1%
15542189551
< 0.1%
15542189601
< 0.1%
ValueCountFrequency (%)
15565491292
< 0.1%
15565486392
< 0.1%
15565483642
< 0.1%
15565479142
< 0.1%
15565474642
< 0.1%
15565473592
< 0.1%
15565473542
< 0.1%
15565473442
< 0.1%
15565472642
< 0.1%
15565472242
< 0.1%

PID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4011
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3256.037293
Minimum1007
Maximum53075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:19.176610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile1371
Q12533
median3058
Q33793
95-th percentile5007
Maximum53075
Range52068
Interquartile range (IQR)1260

Descriptive statistics

Standard deviation2071.338868
Coefficient of variation (CV)0.6361532998
Kurtosis384.5555235
Mean3256.037293
Median Absolute Deviation (MAD)694
Skewness16.25900556
Sum521330643
Variance4290444.707
MonotonicityNot monotonic
2022-11-12T08:03:19.518214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37904183
 
2.6%
37934165
 
2.6%
36753979
 
2.5%
36783965
 
2.5%
36773964
 
2.5%
36763961
 
2.5%
14423960
 
2.5%
27973959
 
2.5%
27743959
 
2.5%
13713954
 
2.5%
Other values (4001)120063
75.0%
ValueCountFrequency (%)
1007158
0.1%
10191
 
< 0.1%
1026136
0.1%
106340
 
< 0.1%
108712
 
< 0.1%
110337
 
< 0.1%
11081
 
< 0.1%
11131
 
< 0.1%
1124121
0.1%
113359
 
< 0.1%
ValueCountFrequency (%)
530751
< 0.1%
530741
< 0.1%
530641
< 0.1%
530601
< 0.1%
530511
< 0.1%
530461
< 0.1%
530452
< 0.1%
530441
< 0.1%
530432
< 0.1%
530421
< 0.1%

TRUN
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08309183571
Minimum0
Maximum9
Zeros147066
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:19.807381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2843146755
Coefficient of variation (CV)3.421692072
Kurtosis24.3423872
Mean0.08309183571
Median Absolute Deviation (MAD)0
Skewness3.724719218
Sum13304
Variance0.08083483469
MonotonicityNot monotonic
2022-11-12T08:03:20.039690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0147066
91.9%
112839
 
8.0%
2183
 
0.1%
313
 
< 0.1%
45
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
0147066
91.9%
112839
 
8.0%
2183
 
0.1%
313
 
< 0.1%
45
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
52
 
< 0.1%
45
 
< 0.1%
313
 
< 0.1%
2183
 
0.1%
112839
 
8.0%
0147066
91.9%

TSLPI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.91681448
Minimum0
Maximum67
Zeros13983
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:20.332062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile13
Maximum67
Range67
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.856579839
Coefficient of variation (CV)2.005859578
Kurtosis31.00150788
Mean3.91681448
Median Absolute Deviation (MAD)1
Skewness5.153660518
Sum627129
Variance61.72584677
MonotonicityNot monotonic
2022-11-12T08:03:20.663517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
174593
46.6%
321720
 
13.6%
415844
 
9.9%
013983
 
8.7%
58249
 
5.2%
26010
 
3.8%
115642
 
3.5%
93691
 
2.3%
172074
 
1.3%
271363
 
0.9%
Other values (36)6943
 
4.3%
ValueCountFrequency (%)
013983
 
8.7%
174593
46.6%
26010
 
3.8%
321720
 
13.6%
415844
 
9.9%
58249
 
5.2%
6403
 
0.3%
7904
 
0.6%
8401
 
0.3%
93691
 
2.3%
ValueCountFrequency (%)
676
 
< 0.1%
665
 
< 0.1%
651
 
< 0.1%
6411
 
< 0.1%
6348
 
< 0.1%
6239
 
< 0.1%
61294
 
0.2%
60909
0.6%
59312
 
0.2%
58138
 
0.1%

TSLPU
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
159969 
1
 
139
2
 
2
21
 
1
17
 
1

Length

Max length2
Median length1
Mean length1.000012491
Min length1

Characters and Unicode

Total characters160114
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0159969
99.9%
1139
 
0.1%
22
 
< 0.1%
211
 
< 0.1%
171
 
< 0.1%

Length

2022-11-12T08:03:20.998949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:21.583137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0159969
99.9%
1139
 
0.1%
22
 
< 0.1%
211
 
< 0.1%
171
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0159969
99.9%
1141
 
0.1%
23
 
< 0.1%
71
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160114
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0159969
99.9%
1141
 
0.1%
23
 
< 0.1%
71
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common160114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0159969
99.9%
1141
 
0.1%
23
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII160114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0159969
99.9%
1141
 
0.1%
23
 
< 0.1%
71
 
< 0.1%

POLI
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
norm
155797 
0
 
2910
-
 
1405

Length

Max length4
Median length4
Mean length3.919150345
Min length1

Characters and Unicode

Total characters627503
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownorm
2nd rownorm
3rd rownorm
4th rownorm
5th rownorm

Common Values

ValueCountFrequency (%)
norm155797
97.3%
02910
 
1.8%
-1405
 
0.9%

Length

2022-11-12T08:03:21.952834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:22.578099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
norm155797
97.3%
02910
 
1.8%
1405
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n155797
24.8%
o155797
24.8%
r155797
24.8%
m155797
24.8%
02910
 
0.5%
-1405
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter623188
99.3%
Decimal Number2910
 
0.5%
Dash Punctuation1405
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n155797
25.0%
o155797
25.0%
r155797
25.0%
m155797
25.0%
Decimal Number
ValueCountFrequency (%)
02910
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin623188
99.3%
Common4315
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n155797
25.0%
o155797
25.0%
r155797
25.0%
m155797
25.0%
Common
ValueCountFrequency (%)
02910
67.4%
-1405
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII627503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n155797
24.8%
o155797
24.8%
r155797
24.8%
m155797
24.8%
02910
 
0.5%
-1405
 
0.2%

NICE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.15747102
Minimum0
Maximum20
Zeros117993
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:22.887660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.722291837
Coefficient of variation (CV)1.691195511
Kurtosis-0.7667436257
Mean5.15747102
Median Absolute Deviation (MAD)0
Skewness1.107347917
Sum825773
Variance76.0783749
MonotonicityNot monotonic
2022-11-12T08:03:23.365055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0117993
73.7%
2040864
 
25.5%
1555
 
0.3%
11434
 
0.3%
10210
 
0.1%
1956
 
< 0.1%
ValueCountFrequency (%)
0117993
73.7%
1555
 
0.3%
10210
 
0.1%
11434
 
0.3%
1956
 
< 0.1%
2040864
 
25.5%
ValueCountFrequency (%)
2040864
 
25.5%
1956
 
< 0.1%
11434
 
0.3%
10210
 
0.1%
1555
 
0.3%
0117993
73.7%

PRI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.6547479
Minimum0
Maximum139
Zeros4315
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:23.910556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1100
median120
Q3120
95-th percentile120
Maximum139
Range139
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.52109038
Coefficient of variation (CV)0.1837905755
Kurtosis20.66816672
Mean111.6547479
Median Absolute Deviation (MAD)0
Skewness-4.33798329
Sum17877265
Variance421.1151502
MonotonicityNot monotonic
2022-11-12T08:03:24.288513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
120113678
71.0%
10040864
 
25.5%
04315
 
2.7%
121555
 
0.3%
109434
 
0.3%
130208
 
0.1%
13956
 
< 0.1%
1102
 
< 0.1%
ValueCountFrequency (%)
04315
 
2.7%
10040864
 
25.5%
109434
 
0.3%
1102
 
< 0.1%
120113678
71.0%
121555
 
0.3%
130208
 
0.1%
13956
 
< 0.1%
ValueCountFrequency (%)
13956
 
< 0.1%
130208
 
0.1%
121555
 
0.3%
120113678
71.0%
1102
 
< 0.1%
109434
 
0.3%
10040864
 
25.5%
04315
 
2.7%

RTPR
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
160112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160112
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0160112
100.0%

Length

2022-11-12T08:03:25.078909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:25.533520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0160112
100.0%

Most occurring characters

ValueCountFrequency (%)
0160112
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0160112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common160112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0160112
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII160112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0160112
100.0%

CPUNR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.569351454
Minimum0
Maximum5
Zeros42811
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:25.939394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.245370844
Coefficient of variation (CV)0.7935576449
Kurtosis-0.6543222896
Mean1.569351454
Median Absolute Deviation (MAD)1
Skewness0.2960978891
Sum251272
Variance1.550948539
MonotonicityNot monotonic
2022-11-12T08:03:26.417755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
042811
26.7%
241052
25.6%
336879
23.0%
133930
21.2%
52841
 
1.8%
42599
 
1.6%
ValueCountFrequency (%)
042811
26.7%
133930
21.2%
241052
25.6%
336879
23.0%
42599
 
1.6%
52841
 
1.8%
ValueCountFrequency (%)
52841
 
1.8%
42599
 
1.6%
336879
23.0%
241052
25.6%
133930
21.2%
042811
26.7%

Status
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
-
125604 
0
29080 
N
 
2759
NE
 
2620
NS
 
47
Other values (2)
 
2

Length

Max length2
Median length1
Mean length1.016663336
Min length1

Characters and Unicode

Total characters162780
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
-125604
78.4%
029080
 
18.2%
N2759
 
1.7%
NE2620
 
1.6%
NS47
 
< 0.1%
C1
 
< 0.1%
NC1
 
< 0.1%

Length

2022-11-12T08:03:26.659456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:26.804241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
125604
78.4%
029080
 
18.2%
n2759
 
1.7%
ne2620
 
1.6%
ns47
 
< 0.1%
c1
 
< 0.1%
nc1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
-125604
77.2%
029080
 
17.9%
N5427
 
3.3%
E2620
 
1.6%
S47
 
< 0.1%
C2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation125604
77.2%
Decimal Number29080
 
17.9%
Uppercase Letter8096
 
5.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N5427
67.0%
E2620
32.4%
S47
 
0.6%
C2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-125604
100.0%
Decimal Number
ValueCountFrequency (%)
029080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common154684
95.0%
Latin8096
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N5427
67.0%
E2620
32.4%
S47
 
0.6%
C2
 
< 0.1%
Common
ValueCountFrequency (%)
-125604
81.2%
029080
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII162780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-125604
77.2%
029080
 
17.9%
N5427
 
3.3%
E2620
 
1.6%
S47
 
< 0.1%
C2
 
< 0.1%

EXC
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01766263615
Minimum0
Maximum100
Zeros159514
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:27.042110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6928328088
Coefficient of variation (CV)39.22590052
Kurtosis13692.86972
Mean0.01766263615
Median Absolute Deviation (MAD)0
Skewness101.0338824
Sum2828
Variance0.4800173009
MonotonicityNot monotonic
2022-11-12T08:03:27.316637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0159514
99.6%
1441
 
0.3%
15107
 
0.1%
919
 
< 0.1%
219
 
< 0.1%
116
 
< 0.1%
1005
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
0159514
99.6%
1441
 
0.3%
219
 
< 0.1%
71
 
< 0.1%
919
 
< 0.1%
116
 
< 0.1%
15107
 
0.1%
1005
 
< 0.1%
ValueCountFrequency (%)
1005
 
< 0.1%
15107
 
0.1%
116
 
< 0.1%
919
 
< 0.1%
71
 
< 0.1%
219
 
< 0.1%
1441
 
0.3%
0159514
99.6%

State
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
S
145383 
R
 
8583
E
 
4315
I
 
1590
D
 
129

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160112
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S145383
90.8%
R8583
 
5.4%
E4315
 
2.7%
I1590
 
1.0%
D129
 
0.1%
Z112
 
0.1%

Length

2022-11-12T08:03:27.817155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:28.422585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
s145383
90.8%
r8583
 
5.4%
e4315
 
2.7%
i1590
 
1.0%
d129
 
0.1%
z112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S145383
90.8%
R8583
 
5.4%
E4315
 
2.7%
I1590
 
1.0%
D129
 
0.1%
Z112
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter160112
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S145383
90.8%
R8583
 
5.4%
E4315
 
2.7%
I1590
 
1.0%
D129
 
0.1%
Z112
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin160112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S145383
90.8%
R8583
 
5.4%
E4315
 
2.7%
I1590
 
1.0%
D129
 
0.1%
Z112
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII160112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S145383
90.8%
R8583
 
5.4%
E4315
 
2.7%
I1590
 
1.0%
D129
 
0.1%
Z112
 
0.1%

CPU
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct237
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03834053662
Minimum0
Maximum4
Zeros116771
Zeros (%)72.9%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-11-12T08:03:28.843064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.08
Maximum4
Range4
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.1884109857
Coefficient of variation (CV)4.914145765
Kurtosis65.43413661
Mean0.03834053662
Median Absolute Deviation (MAD)0
Skewness6.826015102
Sum6138.78
Variance0.03549869953
MonotonicityNot monotonic
2022-11-12T08:03:29.176681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0116771
72.9%
0.0128522
 
17.8%
0.024495
 
2.8%
0.991867
 
1.2%
0.981308
 
0.8%
1923
 
0.6%
0.03895
 
0.6%
0.04429
 
0.3%
0.05385
 
0.2%
0.1378
 
0.2%
Other values (227)4139
 
2.6%
ValueCountFrequency (%)
0116771
72.9%
0.0128522
 
17.8%
0.024495
 
2.8%
0.03895
 
0.6%
0.04429
 
0.3%
0.05385
 
0.2%
0.06282
 
0.2%
0.07160
 
0.1%
0.08224
 
0.1%
0.09350
 
0.2%
ValueCountFrequency (%)
411
< 0.1%
3.851
 
< 0.1%
3.843
 
< 0.1%
3.781
 
< 0.1%
3.741
 
< 0.1%
3.691
 
< 0.1%
3.651
 
< 0.1%
3.631
 
< 0.1%
3.531
 
< 0.1%
3.521
 
< 0.1%

CMD
Categorical

HIGH CARDINALITY

Distinct406
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
atop
40831 
vmtoolsd
9304 
apache2
 
8347
Xorg
 
5873
nautilus
 
5665
Other values (401)
90092 

Length

Max length19
Median length13
Mean length7.954944039
Min length2

Characters and Unicode

Total characters1273682
Distinct characters61
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)0.1%

Sample

1st rowatop
2nd rownautilus
3rd rowupstart-dbus-b
4th rowdrone
5th rowatop

Common Values

ValueCountFrequency (%)
atop40831
25.5%
vmtoolsd9304
 
5.8%
apache28347
 
5.2%
Xorg5873
 
3.7%
nautilus5665
 
3.5%
ostinato5260
 
3.3%
compiz5254
 
3.3%
irqbalance5221
 
3.3%
hud-service5125
 
3.2%
drone4928
 
3.1%
Other values (396)64304
40.2%

Length

2022-11-12T08:03:29.533920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atop40850
25.3%
vmtoolsd9304
 
5.8%
apache29250
 
5.7%
xorg5873
 
3.6%
nautilus5672
 
3.5%
compiz5264
 
3.3%
ostinato5260
 
3.3%
irqbalance5221
 
3.2%
hud-service5125
 
3.2%
drone4928
 
3.1%
Other values (347)64509
40.0%

Most occurring characters

ValueCountFrequency (%)
o128162
 
10.1%
a115833
 
9.1%
t113244
 
8.9%
e96955
 
7.6%
p83900
 
6.6%
n67461
 
5.3%
s65793
 
5.2%
d56935
 
4.5%
i56843
 
4.5%
r52940
 
4.2%
Other values (51)435616
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1155346
90.7%
Dash Punctuation50279
 
3.9%
Decimal Number31753
 
2.5%
Uppercase Letter13430
 
1.1%
Other Punctuation12508
 
1.0%
Math Symbol8626
 
0.7%
Space Separator1144
 
0.1%
Connector Punctuation594
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o128162
11.1%
a115833
 
10.0%
t113244
 
9.8%
e96955
 
8.4%
p83900
 
7.3%
n67461
 
5.8%
s65793
 
5.7%
d56935
 
4.9%
i56843
 
4.9%
r52940
 
4.6%
Other values (16)317280
27.5%
Uppercase Letter
ValueCountFrequency (%)
X5873
43.7%
W3093
23.0%
C2407
17.9%
E708
 
5.3%
M703
 
5.2%
N625
 
4.7%
T6
 
< 0.1%
G4
 
< 0.1%
I4
 
< 0.1%
O4
 
< 0.1%
Other values (3)3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
216413
51.7%
54796
 
15.1%
64688
 
14.8%
02540
 
8.0%
12281
 
7.2%
3810
 
2.6%
7212
 
0.7%
49
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
<4315
50.0%
>4308
49.9%
~2
 
< 0.1%
+1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/6296
50.3%
:6183
49.4%
.29
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-50279
100.0%
Space Separator
ValueCountFrequency (%)
1144
100.0%
Connector Punctuation
ValueCountFrequency (%)
_594
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1168776
91.8%
Common104906
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o128162
 
11.0%
a115833
 
9.9%
t113244
 
9.7%
e96955
 
8.3%
p83900
 
7.2%
n67461
 
5.8%
s65793
 
5.6%
d56935
 
4.9%
i56843
 
4.9%
r52940
 
4.5%
Other values (29)330710
28.3%
Common
ValueCountFrequency (%)
-50279
47.9%
216413
 
15.6%
/6296
 
6.0%
:6183
 
5.9%
54796
 
4.6%
64688
 
4.5%
<4315
 
4.1%
>4308
 
4.1%
02540
 
2.4%
12281
 
2.2%
Other values (12)2807
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1273682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o128162
 
10.1%
a115833
 
9.1%
t113244
 
8.9%
e96955
 
7.6%
p83900
 
6.6%
n67461
 
5.3%
s65793
 
5.2%
d56935
 
4.5%
i56843
 
4.5%
r52940
 
4.2%
Other values (51)435616
34.2%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
100000 
1
60112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160112
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0100000
62.5%
160112
37.5%

Length

2022-11-12T08:03:29.815287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:30.092172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0100000
62.5%
160112
37.5%

Most occurring characters

ValueCountFrequency (%)
0100000
62.5%
160112
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number160112
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100000
62.5%
160112
37.5%

Most occurring scripts

ValueCountFrequency (%)
Common160112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0100000
62.5%
160112
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII160112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0100000
62.5%
160112
37.5%

type
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
normal
100000 
dos
 
10000
ddos
 
10000
injection
 
10000
password
 
10000
Other values (3)
20112 

Length

Max length9
Median length6
Mean length5.936144699
Min length3

Characters and Unicode

Total characters950448
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdos
2nd rowdos
3rd rowdos
4th rowdos
5th rowdos

Common Values

ValueCountFrequency (%)
normal100000
62.5%
dos10000
 
6.2%
ddos10000
 
6.2%
injection10000
 
6.2%
password10000
 
6.2%
scanning10000
 
6.2%
xss10000
 
6.2%
mitm112
 
0.1%

Length

2022-11-12T08:03:30.335642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-12T08:03:30.684844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal100000
62.5%
dos10000
 
6.2%
ddos10000
 
6.2%
injection10000
 
6.2%
password10000
 
6.2%
scanning10000
 
6.2%
xss10000
 
6.2%
mitm112
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n150000
15.8%
o140000
14.7%
a120000
12.6%
r110000
11.6%
m100224
10.5%
l100000
10.5%
s70000
7.4%
d40000
 
4.2%
i30112
 
3.2%
c20000
 
2.1%
Other values (7)70112
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter950448
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n150000
15.8%
o140000
14.7%
a120000
12.6%
r110000
11.6%
m100224
10.5%
l100000
10.5%
s70000
7.4%
d40000
 
4.2%
i30112
 
3.2%
c20000
 
2.1%
Other values (7)70112
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin950448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n150000
15.8%
o140000
14.7%
a120000
12.6%
r110000
11.6%
m100224
10.5%
l100000
10.5%
s70000
7.4%
d40000
 
4.2%
i30112
 
3.2%
c20000
 
2.1%
Other values (7)70112
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII950448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n150000
15.8%
o140000
14.7%
a120000
12.6%
r110000
11.6%
m100224
10.5%
l100000
10.5%
s70000
7.4%
d40000
 
4.2%
i30112
 
3.2%
c20000
 
2.1%
Other values (7)70112
7.4%

Interactions

2022-11-12T08:03:10.870227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:39.556299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:44.553519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:49.173264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:53.605767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:58.742835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:01.880871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:04.360795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:06.773357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:11.275913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:40.447771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:45.010952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:49.634012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:54.015823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:59.119664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:02.107418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:04.548109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:07.411368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:11.740369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:41.077925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:45.452258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:50.005694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:54.616397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:59.309398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:02.348223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:04.751945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:07.775055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:12.048508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:41.475549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:45.876296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:50.384872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:55.442478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:59.604460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:02.565651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:04.926114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:08.151516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:12.427015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:41.949490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:46.428199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:50.986039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:56.164644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:59.986081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:02.931264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:05.154145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:08.726797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:12.846251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:42.501034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:46.965056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:51.482583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:57.053986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:00.358813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:03.235000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:05.382473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:09.240914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:13.275510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:42.994938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:47.609427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:51.864251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:57.737998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:00.802661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:03.460212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:05.571556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:09.701286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:13.791295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:43.440575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:48.215740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:52.278697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:58.070709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:01.180286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:03.752511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:05.832870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:10.095332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:14.201602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:43.865146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:48.704526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:52.963311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:02:58.418196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:01.538805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:04.070227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:06.324085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-12T08:03:10.492009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-12T08:03:31.015583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-12T08:03:31.456095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-12T08:03:31.872954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-12T08:03:32.378049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-12T08:03:32.780472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-12T08:03:33.109446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-12T08:03:15.229769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-12T08:03:16.532752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

tsPIDTRUNTSLPITSLPUPOLINICEPRIRTPRCPUNRStatusEXCStateCPUCMDlabeltype
0155612965852888010norm201000000S0.01atop1dos
115561297382791050norm01200000S0.00nautilus1dos
215561297782504010norm01200200S0.00upstart-dbus-b1dos
3155612978831471120norm01200100S1.00drone1dos
4155612979852888010norm201000000S0.01atop1dos
515561298233144030norm01200000S0.01ostinato1dos
615561298981424010norm01200000S0.00irqbalance1dos
7155612991331471120norm01200100S1.00drone1dos
815561299233144030norm01200100S0.01ostinato1dos
9155612993352886010norm201000300S0.01atop1dos

Last rows

tsPIDTRUNTSLPITSLPUPOLINICEPRIRTPRCPUNRStatusEXCStateCPUCMDlabeltype
16010215542508252753030norm012002-0S0.0ibus-engine-si0normal
16010315542508301931030norm012002-0S0.0upowerd0normal
16010415542508352870020norm012001-0S0.0gvfs-mtp-volum0normal
16010515542508402691040norm012003-0S0.0indicator-soun0normal
16010615542508451452030norm012000-0S0.0accounts-daemo0normal
16010715542508502517020norm012000-0S0.0gvfsd0normal
16010815542508552751030norm012001-0S0.0dconf-service0normal
16010915542508602583040norm012002-0S0.0ibus-dconf0normal
16011015542508652489020norm012003-0S0.0at-spi2-regist0normal
16011115542508702327010norm012001-0S0.0init0normal